基于卷积神经网络模型,提出一种立体图像舒适度评价方法。该方法无须提前根据特定的任务从图像中人工提取具体的特征,而是模拟人脑处理机制对图像进行层次化的抽象处理,自主提取特征。该方法采用三通道卷积神经网络结构,分别对原始图像进行主成分分析,以及32×32、256×256两种尺度的分块处理得到三条通道的输入数据集,根据输入数据设计每条通道的网络结构。采用两种尺寸分块处理得到不同尺寸的图像块特征信息,采用主成分分析降维处理得到原始图像的整体信息。此外,通过随机丢弃、局部响应归一化等方法提升算法的评价性能。实验结果表明,以修正线性单元为激活函数、输出层用Softmax分类器,对天津大学TJU立体图像数据库中400幅不同舒适度等级的立体图像样本进行测试,等级分类率正确达94.52%,优于极限学习机、支持向量机算法。

We propose a new method for stereoscopic image comfort assessment based on convolutional neural network, which does not need to extract specific manual features from images in advance according to specific tasks, but simulates hierarchical abstract processing mechanism of human brain to extract image features autonomously. This method adopts three channel convolutional neural network structure, and the input data sets of the three channel are obtained by reducing the dimension of the original data samples through principal component analysis, and chopping the original data samples into two size image patches (32×32, 256×256), respectively. The network structure of each channel is designed according to the input data sets. In addition, the classification accuracy of this method is improved by introducing dropout and local response normalization, etc. With rectified linear unit as the activation function and Softmax as the classifier in the output layer, experiment results on 400 stereo image samples in TJU database with different comfortable levels show that, the correct classification rate of this method is 94.52%, which is higher than that of the extreme learning machine and support vector machine.